Managed AI infrastructure & cloud operations
Operations for mission-critical GPU environments.
From infrastructure monitoring and incident restoration to change management, root-cause analysis and vendor coordination — operations designed to keep production AI infrastructure available, observable and governed.
Now taking enquiries
Up is not the same as healthy
Conventional monitoring reports whether a server, port or application is up. In AI infrastructure, the costly failures happen while everything still reports as available.
| Layer | Reports as | What can still be wrong |
|---|---|---|
| GPU | Reports as: Online | XID and ECC errors, thermal or power throttling and NVLink faults quietly reduce throughput. |
| Fabric | Reports as: Operational | Congestion and link errors slow the NCCL collectives that distributed training depends on. |
| Storage | Reports as: Available | Throughput and latency fall short of what it takes to keep training jobs fed. |
| Scheduling | Reports as: Healthy | GPUs sit idle or fragmented through inefficient allocation. |
One operational view across every layer
Correlated, not siloed
GPU, fabric, storage, platform and cloud signals are read together, so an incident is traced to its cause rather than its symptoms.
Prioritised by impact
Alerts are ranked by their effect on workloads and services, not by the status of a single device.
Specialist depth, without the headcount
Expertise in GPU infrastructure, InfiniBand, RDMA, storage, Kubernetes and cloud, without building a large in-house operations team.
AI & HPC Infrastructure
The layers that training and inference depend on: GPUs, fabric, storage and scheduling.
GPU & Server Operations
Fleet health from NVIDIA DCGM, NVSM and platform telemetry, with approved workflows to isolate unhealthy nodes, drain workloads and start recovery.
- Signals
- XID and ECC events · Thermals and power · Performance throttling · NVLink and NVSwitch · PCIe errors · Node availability
High-Speed Fabric Operations
InfiniBand and high-performance Ethernet run for the bandwidth distributed workloads need, not only for connectivity.
- Signals
- Link and port health · Congestion · Error counters · Topology
- Troubleshooting
- RDMA · GPUDirect · NCCL
AI Storage Operations
Distributed storage measured against the throughput and latency your workloads require.
- Signals
- Throughput · Latency · Client connectivity · Capacity · Performance degradation
- Platforms
- VAST Data · Weka · NetApp ONTAP · StorageGRID · NFS · NFS over RDMA
Platform & Orchestration
The Kubernetes and Slurm layer that turns hardware into schedulable GPU capacity.
- Coverage
- Kubernetes control planes · Slurm · NVIDIA GPU Operator · NVIDIA Network Operator · Node lifecycle · GPU allocation · Quotas and isolation
Cloud Infrastructure
Public, private and hybrid cloud, operated alongside your GPU estate.
Compute & Virtualisation
Instances and virtual machines kept healthy and sized to demand.
- Signals
- CPU · Memory · Disk I/O · Availability · Autoscaling · Operating system health
Cloud Networking
Connectivity across clouds, regions and on-premises sites, with routing and latency issues traced end to end.
- Coverage
- VPCs and VNets · Transit gateways · VPN and SD-WAN · Cloud interconnects · Ingress and load balancing
- Interconnects
- AWS Direct Connect · Azure ExpressRoute
Storage & Databases
Cloud storage and managed databases watched for availability, replication, capacity and performance.
- Platforms
- Amazon EBS · Azure Managed Disks · Object storage · Relational databases · NoSQL services
Security & Operational Hygiene
Configuration and access changes flagged early, with regular cost and utilisation reviews for rightsizing.
- Signals
- Configuration drift · IAM changes · Security-group exposure · Backup status · Cost and utilisation
Application & Workload Observability
From infrastructure health to the experience of your users.
Application Performance
Performance traced through the application runtime, down to the transaction.
- Signals
- Latency · Transactions · Exceptions · Thread contention · Memory and garbage collection
- Runtimes
- Java · Go · Python · Node.js
Kubernetes & Service Mesh
Workload and service-to-service visibility across clusters.
- Coverage
- Pods · Nodes · DaemonSets · Ingress · Service-to-service traffic
- Service mesh
- Istio · Envoy
Synthetic & End-User Monitoring
Critical journeys tested continuously from multiple locations.
- Journeys
- Login · Checkout · API availability · SSL certificates · Business workflows
Databases & Middleware
Bottlenecks found in the data and messaging layers before they surface as outages.
- Signals
- Slow queries · Connection pools · Queue backlogs · Cache performance
- Platforms
- Kafka · RabbitMQ · Redis · Memcached
Service Management & Vendor Coordination
Disciplined workflows, and one owner across your hardware and software suppliers.
IT Service Management
Incidents, problems, changes and requests run through defined workflows, with clear escalation and post-incident review.
- Processes
- Incident · Problem · Change · Request · Escalation · Post-incident review · Root-cause analysis
Vendor & OEM Management
One owner for supplier tickets, from the first evidence to confirmed closure.
- Coverage
- Ticket ownership · Evidence gathering · Vendor escalation · Replacement coordination · Closure tracking
How the service runs
One operating flow, from the first signal to lasting improvement. Coverage, support hours and responsibilities are agreed in the service contract for each environment.
Observe
Continuous monitoring and alert management across infrastructure and applications.
Detect
Degradation in GPU, network, storage and application performance identified before it affects production workloads.
Triage
Related alerts correlated and prioritised by service impact, not device status.
Respond
Approved runbooks automated where practical; complex incidents escalated to specialists in GPU, InfiniBand, RDMA, storage, Kubernetes and cloud.
Restore
Restoration coordinated across compute, network, storage and hardware vendors.
Analyse
Root-cause analysis and problem management for recurring issues.
Improve
Service reviews, capacity planning and change governance.
Service management
| Incident | Severity-based response and coordinated restoration. |
|---|---|
| Problem | Recurring-issue analysis and corrective-action tracking. |
| Change | Controlled changes with approval and implementation records. |
| Reporting | Operational reporting and a regular service-review cadence. |
| Escalation | Defined operational and management escalation paths. |
Defined in writing before service starts
Good operations start with a clear agreement on what is covered and who does what.
Covered infrastructure
The hardware, software and infrastructure layers in scope, and what is excluded.
Support coverage
Support hours, responsibilities and the escalation matrix.
Processes
How incidents, problems and changes are raised, approved and recorded.
Measures and reviews
Service measures, reporting and the governance cadence.
Access and vendors
Access, onboarding and coordination with your hardware and software vendors.
Common questions
Does Managed Services cover every GPU, network and storage vendor?
Coverage depends on your environment and the agreed service scope. Tell us which vendors, platforms and support contracts are involved when you make an enquiry.
Are support hours and response times guaranteed?
Support hours and any response targets are set in the service agreement for your environment. The website does not promise a standard response time.
Can Managed Services be combined with GPU capacity from Varni?
Yes. Capacity and operations are separate enquiries, and they can be scoped together.
Discuss your environment
Tell us about your infrastructure and the operations coverage you need.
Now taking enquiries